Paper Info
Title
Context-Aware Mobile Intelligent Transportation Systems
Abstract
This paper proposes a practical quantification model for mobile phone based traffic state estimation systems (M-TES). The low penetration rate issue, an inherent issue impeding the realization of a mobile phone based application such as the M-TES, is thoroughly discussed. A notable solution framework, namely the intelligent context-aware velocity-density inference circuit (ICIC), is proposed to effectively resolve the low penetration rate issue. In the ICIC model, velocities and densities calculated directly from the sensed data and inferred by using different inference models such as the Greeshields or the moving average model are appropriately integrated. In addition, appropriate contexts extracted from data reported by mobile devices are utilized to identify the optimal estimation parameters leading to the optimal estimation effectiveness. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.
Year
DOI
Venue
2012
10.1109/VTCFall.2012.6398916
VTC Fall
Keywords
Field
DocType
mobile phone based traffic state estimation systems,mobile devices,mobile handsets,intelligent context-aware velocity-density inference circuit,icic,inference mechanisms,context-aware mobile intelligent transportation systems,moving average model,low penetration rate,automated highways,m-tes,inference models,mobile computing,optimal estimation parameters
Mobile computing,Inference,Computer science,Real-time computing,Robustness (computer science),Optimal estimation,Mobile device,Mobile phone,Intelligent transportation system,Genetic algorithm
Conference
ISSN
ISBN
Citations 
1090-3038 E-ISBN : 978-1-4673-1879-2
978-1-4673-1879-2
0
PageRank 
References 
Authors
0.34
5
3
Name
Order
Citations
PageRank
Quang Tran Minh19722.78
Muhammad Ariff Baharudin242.13
Eiji Kamioka39621.65